System identification for the Berkeley lower extremity exoskeleton (BLEEX)

Ghan, Justin; Kazerooni, H.

doi:10.1109/robot.2006.1642233

Cited by 68 publications

(29 citation statements)

References 3 publications

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“…Therefore, there were totally 46 sensors of various kinds incorporated in the system [14] . While this control strategy led to high sensitivity, its robustness tended to suffer, which rendered the system highly dependent upon active models, and which necessitated a large number of experiments to optimize all the parameters [15][16] . The BLEEX used a small diesel engine as power supply, and was packed a 4 L fuel tank [17] , because the team leader believed that hydrocarbon fuel was still the lightest energy source available.…”

Section: Active Exoskeletonsmentioning

confidence: 99%

Proceeding of human exoskeleton technology and discussions on future research

Xie

et al. 2014

Chin. J. Mech. Eng.

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After more than half a century of intense efforts, the development of exoskeleton has seen major advances, and several remarkable achievements have been made. Reviews of developing history of exoskeleton are presented, both in active and passive categories. Major models are introduced, and typical technologies are commented on. Difficulties in control algorithm, driver system, power source, and man-machine interface are discussed. Current researching routes and major developing methods are mapped and critically analyzed, and in the process, some key problems are revealed. First, the exoskeleton is totally different from biped robot, and relative studies based on the robot technologies are considerably incorrect. Second, biomechanical studies are only used to track the motion of the human body, the interaction between human and machines are seldom studied. Third, the traditional developing ways which focused on servo-controlling have inborn deficiency from making portable systems. Research attention should be shifted to the human side of the coupling system, and the human ability to learn and adapt should play a more significant role in the control algorithms. Having summarized the major difficulties, possible future works are discussed. It is argued that, since a distinct boundary cannot be drawn in such strong-coupling human-exoskeleton system, the more complex the control system gets, the more difficult it is for the user to learn to use. It is suggested that the exoskeleton should be treated as a simple wearable tool, and downgrading its automatic level may be a change toward a brighter research outlook. This effort at simplification is definitely not easy, as it necessitates theoretical supports from fields such as biomechanics, ergonomics, and bionics.

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Section: Active Exoskeletonsmentioning

confidence: 99%

Proceeding of human exoskeleton technology and discussions on future research

Xie

et al. 2014

Chin. J. Mech. Eng.

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“…Therefore, using the error between the identified torque and the measured torque to evaluate the identification accuracy is consistent with the original intention and application of exoskeleton parameter identification. The method of evaluating identification accuracy by the error between identified torque and measured torque has been verified and applied [21,32]. Ghan et al evaluated the identification accuracy by the agreement between the identified torque and measured torque [21].…”

Section: Quantitative Evaluation Methods For Identification Accuracymentioning

confidence: 99%

“…The method of evaluating identification accuracy by the error between identified torque and measured torque has been verified and applied [21,32]. Ghan et al evaluated the identification accuracy by the agreement between the identified torque and measured torque [21]. Bing et al used the comparison of identified torque and measured torque for evaluating the identification accuracy and comparing between PSO and LS.…”

Section: Quantitative Evaluation Methods For Identification Accuracymentioning

confidence: 99%

“…Third, obtaining the dynamic parameters through dynamic parameter identification algorithms. This method has high accuracy and requires a series of experiments for data acquisition [20][21][22]. The exoskeleton is made up of many non-standard parts, such as pipes and actuators [6][7][8][9][10], which cause the dynamic parameters of the exoskeleton to fluctuate during working.…”

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confidence: 99%

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Dynamic Parameter Identification of a Lower Extremity Exoskeleton Using RLS-PSO

et al. 2019

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The lower extremity exoskeleton is a device for auxiliary assistance of human movement. The interaction performance between the exoskeleton and the human is determined by the lower extremity exoskeleton's controller. The performance of the controller is affected by the accuracy of the dynamic equation. Therefore, it is necessary to study the dynamic parameter identification of lower extremity exoskeleton. The existing dynamic parameter identification algorithms for lower extremity exoskeletons are generally based on Least Square (LS). There are some internal drawbacks, such as complicated experimental processes and low identification accuracy. A dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by Recursive Least Square (RLS) is developed in this investigation. The developed algorithm is named RLS-PSO. By defining the search space of PSO, RLS-PSO not only avoids the convergence of identified parameters to the local minima, but also improves the identification accuracy of exoskeleton dynamic parameters. Under the same experimental conditions, the identification accuracy of RLS-PSO, PSO and LS was quantitatively compared and analyzed. The results demonstrated that the identification accuracy of RLS-PSO is higher than that of LS and PSO. Appl. Sci. 2019, 9, 324 2 of 17 dynamics of the exoskeleton. Third, obtaining the dynamic parameters through dynamic parameter identification algorithms. This method has high accuracy and requires a series of experiments for data acquisition [20][21][22]. The exoskeleton is made up of many non-standard parts, such as pipes and actuators [6][7][8][9][10], which cause the dynamic parameters of the exoskeleton to fluctuate during working. Obviously, it is difficult to obtain the dynamic parameters from the manufacturers or 3D design software. Therefore, parameter identification algorithms are expected to be an effective way to obtain more accurate dynamic parameters [23,24].In order to obtain more accurate dynamic parameters, researchers are increasingly paying attention to the application of parameter identification algorithms for the exoskeleton. Researchers have carried out some preliminary research works. Targeting the swing phase [25,26], Justin et al. identified the dynamic parameters of Berkeley Exoskeleton (BLEEX) [21] by means of Least Square (LS) [27,28]. The joint friction coefficients of BLEEX were identified by static experiments, and the inertial parameters [29] were identified by dynamic experiments. The method did not make full use of the non-correlation between the parameters and the linear relationship between the parameters and the parameterized dynamic equations [30,31]. Therefore, the method only identified one parameter in each experiment. Designing a corresponding identification experiment for each parameter was necessary. The process of parameter identification experiment was very complicated, and the friction parameters on the hip joint lost identifiability because of the limited range of motion. ...

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“…Recognition and Machine Intelligence Lab) PD PD 1 BLEEX [1][2][3] BLEEX 45 2 / BLEEX [4] HAL [5][6][7] HAL [8] [ PD-GC) [14] PRMI PD (Fuzzy PD Control with Dynamic Model : …”

Section: Prmi (Exoskeleton In Patternmentioning

confidence: 99%

Study on master-slave control strategy of lower extremity exoskeleton robot

Huang

Cheng

Zheng

et al. 2014

Proceeding of the 11th World Congress on Intelligent Control and Automation

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This paper presents a control algorithm for lower extremity exoskeleton robot which based on master-slave control strategy, First the lower extremity exoskeleton 'PRMI exoskeleton' (exoskeleton developed in Pattern Recognition and MachineIntelligence Lab) is introduced in this paper, which divided into three parts including a mechanical system, a sensing system and a control system. Then a kind of control algorithm based on fuzzy PD controller with dynamic model compensation is proposed for exoskeleton control. Finally the algorithm is validated in the real-time system, and high performance of the algorithm in these experiments illustrate its superiority in master-slave control.

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System identification for the Berkeley lower extremity exoskeleton (BLEEX)

Cited by 68 publications

References 3 publications

Proceeding of human exoskeleton technology and discussions on future research

Proceeding of human exoskeleton technology and discussions on future research

Dynamic Parameter Identification of a Lower Extremity Exoskeleton Using RLS-PSO

Study on master-slave control strategy of lower extremity exoskeleton robot

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